#!coding:utf-8
"""Identity Mappings in Deep Residual Networks
"""
import torch
import torch.nn as nn
import torch.nn.functional as F

class PreActBlock(nn.Module):
    expansion = 1
    
    def __init__(self, in_planes, planes, stride=1):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)

        if stride!=1 or in_planes!=self.expansion*planes:
            self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out += shortcut
        return out

class PreActBottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)

        if stride!=1 or in_planes!=self.expansion*planes:
            self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out = self.conv3(F.relu(self.bn3(out)))
        out += shortcut
        return out

class PreActResNet(nn.Module):
    
    def __init__(self, block, num_blocks, num_classes):
        super(PreActResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.fc1 = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0),-1)
        return self.fc1(out)

def PreActResNet18(num_classes):
    return PreActResNet(PreActBlock, [2,2,2,2], num_classes)

def PreActResNet34(num_classes):
    return PreActResNet(PreActBlock, [3,4,6,3], num_classes)

def PreActResNet50(num_classes):
    return PreActResNet(PreActBottleneck, [3,4,6,3], num_classes)

def PreActResNet101(num_classes):
    return PreActResNet(PreActBottleneck, [3,4,23,3], num_classes)

def PreActResNet152(num_classes):
    return PreActResNet(PreActBottleneck, [3,8,36,3], num_classes)

def test():
    print('--- run preact_resnet test')
    x = torch.randn(2,3,32,32)
    for net in [PreActResNet18(10), PreActResNet34(10), PreActResNet50(10), PreActResNet101(10), PreActResNet152(10)]:
        print(net)
        y = net(x)
        print(y.size())
